0000000000305629
AUTHOR
Abhishek Desai
IceCube-Gen2: The Window to the Extreme Universe
The observation of electromagnetic radiation from radio to $\gamma$-ray wavelengths has provided a wealth of information about the universe. However, at PeV (10$^{15}$ eV) energies and above, most of the universe is impenetrable to photons. New messengers, namely cosmic neutrinos, are needed to explore the most extreme environments of the universe where black holes, neutron stars, and stellar explosions transform gravitational energy into non-thermal cosmic rays. The discovery of cosmic neutrinos with IceCube has opened this new window on the universe. In this white paper, we present an overview of a next-generation instrument, IceCube-Gen2, which will sharpen our understanding of the proce…
EV-Scale Sterile Neutrino Search Using Eight Years of Atmospheric Muon Neutrino Data from the IceCube Neutrino Observatory
Physical review letters 125(14), 141801 (1-11) (2020). doi:10.1103/PhysRevLett.125.141801
LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories
We present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction processes relevant for neutrino observatories: neutrino-nucleon deep-inelastic scattering and neutrino-electron annihilation. In this paper, we discuss the event generation algorithm, the weighting algorithm, and the main functions of the publicly available code, with examples.
A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction …